A novel approach to robot vision using a hexagonal grid and spiking neural networks

D Kerr, SA Coleman, TM McGinnity, Qingxiang Wu, M. Clogenson

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)
5 Downloads (Pure)

Abstract

Many robots use range data to obtain an almost 3-dimensional description of their environment. Feature driven segmentation of range images has been primarily used for 3D object recognition, and hence the accuracy of the detected features is a prominent issue. Inspired by the structure and behaviour of the human visual system, we present an approach to feature extraction in range data using spiking neural networks and a biologically plausible hexagonal pixel arrangement. Standard digital images are converted into a hexagonal pixel representation and then processed using a spiking neural network with hexagonal shaped receptive fields; this approach is a step towards developing a robotic eye that closely mimics the human eye. The performance is compared with receptive fields implemented on standard rectangular images. Results illustrate that, using hexagonally shaped receptive fields, performance is improved over standard rectangular shaped receptive fields.
Original languageEnglish
Title of host publicationUnknown Host Publication
PublisherIEEE
Number of pages7
ISBN (Print)978-1-4673-1488-6
DOIs
Publication statusPublished - 30 Jul 2012
EventThe 2012 International Joint Conference on Neural Networks (IJCNN) - Brisbane, Australia
Duration: 30 Jul 2012 → …

Conference

ConferenceThe 2012 International Joint Conference on Neural Networks (IJCNN)
Period30/07/12 → …

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